{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 如何统计收益率？\n",
    "*价格变化的百分比的概率统计*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 目录\n",
    "1. 怎么计算累积收益率？\n",
    "2. 如何计算收益率的均值与方差？\n",
    "3. 如何用图显示收益分布？\n",
    "4. 如何计算百分位？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 怎么计算累积收益率？\n",
    "\n",
    "一天收益率 = $\\frac {P_t-P_{t-1}}{P_t}$\n",
    "\n",
    "五天收益率 = $\\frac {P_t-P_{t-5}}{P_t}$\n",
    "\n",
    "十天收益率 = $\\frac {P_t-P_{t-10}}{P_t}$\n",
    "\n",
    "累积收益率 = $cumprod (1+r_n)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date\n",
      "2016-01-05    0.995957\n",
      "2016-01-06    1.000622\n",
      "2016-01-07    0.963615\n",
      "2016-01-08    0.971700\n",
      "2016-01-11    0.927168\n",
      "Name: close, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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sXFc8XBy5f+kYfvDabl7fnsWS8aHc+eIO8ivqSS+q5p/XTMTRwUJtQzNv78pm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\nJHmn6hUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe66e668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tushare as ts\n",
    "import pandas as pd\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "stock = ts.get_k_data('600036', start='2016-01-01', end='2016-12-31', \n",
    "                      ktype='D', autype='qfq')\n",
    "\n",
    "stock.index = pd.to_datetime(stock['date'], format='%Y-%m-%d')\n",
    "stock.pop('date')\n",
    "# stock = pd.DataFrame(stock, dtype=np.float64)\n",
    "\n",
    "returns = stock.close.pct_change()[1:]\n",
    "r_add1 = returns+1\n",
    "cumulative_r = r_add1.cumprod()\n",
    "print cumulative_r.head()\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "plt.plot(cumulative_r)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何计算收益率的均值与方差？\n",
    "**均值计算：**\n",
    "\n",
    "|类型|描述|例子|结果|\n",
    "|:--:| :--:| :-----------: |:-----:|\n",
    "|算术均值 Arithmetic Mean|数据的和除以数据的数量|(1+2+2+3+4+7+9) / 7|4|\n",
    "|中值 Median|中间的那个值，把数据分成大小两半|1, 2, 2, 3, 4, 7, 9|3|\n",
    "|众数 Mode|频度最大的那个数|1, 2, 2, 3, 4, 7, 9|2|\n",
    "\n",
    "**方差与标准差：**\n",
    "\n",
    "各数据偏离平均数的距离（离均差）的平均数。\n",
    "\n",
    "\n",
    "$\\sigma^2=\\frac{1}{n-1}\\sum^n_{i=1}(|x_i-\\bar{x}|)^2$\n",
    "\n",
    "$\\sigma=\\sqrt{\\frac{1}{n-1}\\sum^n_{i=1}(|x_i-\\bar{x}|)^2}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('mean', 0.0003079110510024028)\n",
      "('median', 0.0005715918833950617)\n",
      "('mode', 0    0.0\n",
      "dtype: float64)\n",
      "('var', 0.00014715765936232873)\n",
      "('std', 0.012130855673130759)\n"
     ]
    }
   ],
   "source": [
    "r = returns.dropna()\n",
    "\n",
    "mean = r.mean()\n",
    "median = r.median()\n",
    "mode = r.mode()\n",
    "var = r.var()\n",
    "std = r.std()\n",
    "\n",
    "print('mean', mean)\n",
    "print('median', median)\n",
    "print('mode', mode)\n",
    "print('var', var)\n",
    "print('std', std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何用图显示收益分布？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZLj+VjIwM2rdvT5cuXWjXrl3W/B49/ry/r776iu+//57//ve/vPTSS6xateqk+zPG/G1e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\niIiISI34f1gPt6y9YcV6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfb1d4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "x = np.linspace(-(mean + 4*std),\n",
    "                (mean + 4*std), len(returns))\n",
    "\n",
    "y = stats.norm.pdf(x, mean, std)\n",
    "\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "plt.plot(x, y)\n",
    "plt.hist(returns[1:], bins = 20)\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Occurrences')\n",
    "plt.vlines(mean, 0, 60, linestyles='dashed'\n",
    "           , alpha=0.5, color='g', label='mean')\n",
    "plt.vlines([std, -std],  0, 25, linestyles='dashed'\n",
    "           , alpha=0.5, color='r', label='std(68.3%)')\n",
    "plt.vlines([2*std, -2*std], 0, 10, linestyles='dashed'\n",
    "           , alpha=0.5, color='y', label='2*std(95%)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何计算百分位？\n",
    "百分位数 Percentile：如第五百分位，它表示在所有测量数据中，测量值的累计频次达5%。以回报率为例，回报率分布的第五百分位表示有5%的回报率小于此测量值，95%的回报率大于此测量值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.0185866304046\n",
      "0.019414104474\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xf0f4400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print returns.quantile(0.05)\n",
    "print returns.quantile(0.95)\n",
    "\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "plt.hist(returns[1:], bins = 20)\n",
    "plt.vlines([returns.quantile(0.05), returns.quantile(0.95)], 0, 15, linestyles='dashed'\n",
    "           , alpha=0.5, color='r', label='Quantile5')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业\n",
    "换一只股票计算分布与百分位"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
